Traffic information prediction is one of the most essential studies for traffic research, operation and management. The successful prediction of traffic speed is increasingly significant for the benefits of both road users and traffic authorities. However, accurate prediction is challenging, due to the stochastic feature of traffic flow and shallow model structure. Furthermore, environmental factors, such as rainfall influence, should also be incorporated to improve accuracy. Inspired by deep learning, this paper investigates the performance of deep belief network (DBN) and long short-term memory (LSTM) to conduct short-term traffic speed prediction with the consideration of rainfall impact as a non-traffic input. The deep learning models have the ability to learn complex features of traffic flow pattern under various rainfall conditions. To validate the performance of rainfall-integrated DBN and LSTM, the traffic detector data from an arterial in Beijing are utilised for model training and testing. The experiment results indicate that with the combination input of speed and additional rainfall data, deep learning models have better prediction accuracy over other existing models, and also yields improvements over the models without rainfall input. Furthermore, the LSTM can outperform the DBN to capture the time-series characteristics of traffic speed data.
Rainfall-integrated traffic speed prediction using deep learning method
IET Intelligent Transport Systems ; 11 , 9 ; 531-536
2017-08-10
6 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DBN , traffic information systems , Beijing , belief networks , traffic information prediction , rainfall influence , learning (artificial intelligence) , traffic authorities , deep testing , long short-term memory , short-term traffic speed prediction , time series , deep training , LSTM , traffic operation , rainfall-integrated traffic speed prediction , road users , traffic flow , stochastic feature , deep belief network , environmental factors , traffic management , time-series characteristics , deep learning method , traffic research , traffic detector data , shallow model structure , nontraffic input
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